Paper Info
Title
Reconstruction of linearly parameterized models three vanishing points from a single image of perspective projection.
Abstract
We present a method using only three vanishing points to recover the dimensions of object and its pose from a single image of perspective projection with a camera of unknown focal length. Our approach is to compute the dimensions of objects which are represented by the unit vector of objects from the image. The dimension vector v for the objects can be solved by the standard nonlinear optimization techniques with a multistart method which generates multiple starting points for the optimizer by sampling the parameter space uniformly. This method allows model-based vision to be computed the dimensions of object for a 3D model from matches to a single 2D image. Therefore, experimental results show the evaluated values of the object dimensions from a single image using three vanishing points. In addition, the actual dimensions of object from the image agree well with the calculated results.
Year
DOI
Venue
2003
10.1109/ISSPA.2003.1224628
SEVENTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOL 1, PROCEEDINGS
Keywords
Field
DocType
perspective projection,geometry,computer vision,image reconstruction,computer architecture,vanishing point,parameter space,computer graphics,solid modeling,focal length,nonlinear optimization
Iterative reconstruction,Computer vision,Computer science,Nonlinear programming,Focal length,Perspective (graphical),Solid modeling,Artificial intelligence,Parameter space,Vanishing point,Unit vector
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
6
Name
Order
Citations
PageRank
Yong-in Yoon1143.20
Jang-Hwan Im211.39
Jong-Soo Choi314730.10
Jin-tae Kim415021.35
Dong-wook Kim5172.94
Jun-sik Kwon6212.69